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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>5.11. Table Partitioning</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="ddl-inherit.html" title="5.10. Inheritance" /><link rel="next" href="ddl-foreign-data.html" title="5.12. Foreign Data" /></head><body id="docContent" class="container-fluid col-10"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">5.11. Table Partitioning</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="ddl-inherit.html" title="5.10. Inheritance">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="ddl.html" title="Chapter 5. Data Definition">Up</a></td><th width="60%" align="center">Chapter 5. Data Definition</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 16.2 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="ddl-foreign-data.html" title="5.12. Foreign Data">Next</a></td></tr></table><hr /></div><div class="sect1" id="DDL-PARTITIONING"><div class="titlepage"><div><div><h2 class="title" style="clear: both">5.11. Table Partitioning <a href="#DDL-PARTITIONING" class="id_link">#</a></h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="ddl-partitioning.html#DDL-PARTITIONING-OVERVIEW">5.11.1. Overview</a></span></dt><dt><span class="sect2"><a href="ddl-partitioning.html#DDL-PARTITIONING-DECLARATIVE">5.11.2. Declarative Partitioning</a></span></dt><dt><span class="sect2"><a href="ddl-partitioning.html#DDL-PARTITIONING-USING-INHERITANCE">5.11.3. Partitioning Using Inheritance</a></span></dt><dt><span class="sect2"><a href="ddl-partitioning.html#DDL-PARTITION-PRUNING">5.11.4. Partition Pruning</a></span></dt><dt><span class="sect2"><a href="ddl-partitioning.html#DDL-PARTITIONING-CONSTRAINT-EXCLUSION">5.11.5. Partitioning and Constraint Exclusion</a></span></dt><dt><span class="sect2"><a href="ddl-partitioning.html#DDL-PARTITIONING-DECLARATIVE-BEST-PRACTICES">5.11.6. Best Practices for Declarative Partitioning</a></span></dt></dl></div><a id="id-1.5.4.13.2" class="indexterm"></a><a id="id-1.5.4.13.3" class="indexterm"></a><a id="id-1.5.4.13.4" class="indexterm"></a><p>
<span class="productname">PostgreSQL</span> supports basic table
partitioning. This section describes why and how to implement
partitioning as part of your database design.
</p><div class="sect2" id="DDL-PARTITIONING-OVERVIEW"><div class="titlepage"><div><div><h3 class="title">5.11.1. Overview <a href="#DDL-PARTITIONING-OVERVIEW" class="id_link">#</a></h3></div></div></div><p>
Partitioning refers to splitting what is logically one large table into
smaller physical pieces. Partitioning can provide several benefits:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
Query performance can be improved dramatically in certain situations,
particularly when most of the heavily accessed rows of the table are in a
single partition or a small number of partitions. Partitioning
effectively substitutes for the upper tree levels of indexes,
making it more likely that the heavily-used parts of the indexes
fit in memory.
</p></li><li class="listitem"><p>
When queries or updates access a large percentage of a single
partition, performance can be improved by using a
sequential scan of that partition instead of using an
index, which would require random-access reads scattered across the
whole table.
</p></li><li class="listitem"><p>
Bulk loads and deletes can be accomplished by adding or removing
partitions, if the usage pattern is accounted for in the
partitioning design. Dropping an individual partition
using <code class="command">DROP TABLE</code>, or doing <code class="command">ALTER TABLE
DETACH PARTITION</code>, is far faster than a bulk
operation. These commands also entirely avoid the
<code class="command">VACUUM</code> overhead caused by a bulk <code class="command">DELETE</code>.
</p></li><li class="listitem"><p>
Seldom-used data can be migrated to cheaper and slower storage media.
</p></li></ul></div><p>
These benefits will normally be worthwhile only when a table would
otherwise be very large. The exact point at which a table will
benefit from partitioning depends on the application, although a
rule of thumb is that the size of the table should exceed the physical
memory of the database server.
</p><p>
<span class="productname">PostgreSQL</span> offers built-in support for the
following forms of partitioning:
</p><div class="variablelist"><dl class="variablelist"><dt id="DDL-PARTITIONING-OVERVIEW-RANGE"><span class="term">Range Partitioning</span> <a href="#DDL-PARTITIONING-OVERVIEW-RANGE" class="id_link">#</a></dt><dd><p>
The table is partitioned into <span class="quote">“<span class="quote">ranges</span>”</span> defined
by a key column or set of columns, with no overlap between
the ranges of values assigned to different partitions. For
example, one might partition by date ranges, or by ranges of
identifiers for particular business objects.
Each range's bounds are understood as being inclusive at the
lower end and exclusive at the upper end. For example, if one
partition's range is from <code class="literal">1</code>
to <code class="literal">10</code>, and the next one's range is
from <code class="literal">10</code> to <code class="literal">20</code>, then
value <code class="literal">10</code> belongs to the second partition not
the first.
</p></dd><dt id="DDL-PARTITIONING-OVERVIEW-LIST"><span class="term">List Partitioning</span> <a href="#DDL-PARTITIONING-OVERVIEW-LIST" class="id_link">#</a></dt><dd><p>
The table is partitioned by explicitly listing which key value(s)
appear in each partition.
</p></dd><dt id="DDL-PARTITIONING-OVERVIEW-HASH"><span class="term">Hash Partitioning</span> <a href="#DDL-PARTITIONING-OVERVIEW-HASH" class="id_link">#</a></dt><dd><p>
The table is partitioned by specifying a modulus and a remainder for
each partition. Each partition will hold the rows for which the hash
value of the partition key divided by the specified modulus will
produce the specified remainder.
</p></dd></dl></div><p>
If your application needs to use other forms of partitioning not listed
above, alternative methods such as inheritance and
<code class="literal">UNION ALL</code> views can be used instead. Such methods
offer flexibility but do not have some of the performance benefits
of built-in declarative partitioning.
</p></div><div class="sect2" id="DDL-PARTITIONING-DECLARATIVE"><div class="titlepage"><div><div><h3 class="title">5.11.2. Declarative Partitioning <a href="#DDL-PARTITIONING-DECLARATIVE" class="id_link">#</a></h3></div></div></div><p>
<span class="productname">PostgreSQL</span> allows you to declare
that a table is divided into partitions. The table that is divided
is referred to as a <em class="firstterm">partitioned table</em>. The
declaration includes the <em class="firstterm">partitioning method</em>
as described above, plus a list of columns or expressions to be used
as the <em class="firstterm">partition key</em>.
</p><p>
The partitioned table itself is a <span class="quote">“<span class="quote">virtual</span>”</span> table having
no storage of its own. Instead, the storage belongs
to <em class="firstterm">partitions</em>, which are otherwise-ordinary
tables associated with the partitioned table.
Each partition stores a subset of the data as defined by its
<em class="firstterm">partition bounds</em>.
All rows inserted into a partitioned table will be routed to the
appropriate one of the partitions based on the values of the partition
key column(s).
Updating the partition key of a row will cause it to be moved into a
different partition if it no longer satisfies the partition bounds
of its original partition.
</p><p>
Partitions may themselves be defined as partitioned tables, resulting
in <em class="firstterm">sub-partitioning</em>. Although all partitions
must have the same columns as their partitioned parent, partitions may
have their
own indexes, constraints and default values, distinct from those of other
partitions. See <a class="xref" href="sql-createtable.html" title="CREATE TABLE"><span class="refentrytitle">CREATE TABLE</span></a> for more details on
creating partitioned tables and partitions.
</p><p>
It is not possible to turn a regular table into a partitioned table or
vice versa. However, it is possible to add an existing regular or
partitioned table as a partition of a partitioned table, or remove a
partition from a partitioned table turning it into a standalone table;
this can simplify and speed up many maintenance processes.
See <a class="xref" href="sql-altertable.html" title="ALTER TABLE"><span class="refentrytitle">ALTER TABLE</span></a> to learn more about the
<code class="command">ATTACH PARTITION</code> and <code class="command">DETACH PARTITION</code>
sub-commands.
</p><p>
Partitions can also be <a class="link" href="ddl-foreign-data.html" title="5.12. Foreign Data">foreign
tables</a>, although considerable care is needed because it is then
the user's responsibility that the contents of the foreign table
satisfy the partitioning rule. There are some other restrictions as
well. See <a class="xref" href="sql-createforeigntable.html" title="CREATE FOREIGN TABLE"><span class="refentrytitle">CREATE FOREIGN TABLE</span></a> for more
information.
</p><div class="sect3" id="DDL-PARTITIONING-DECLARATIVE-EXAMPLE"><div class="titlepage"><div><div><h4 class="title">5.11.2.1. Example <a href="#DDL-PARTITIONING-DECLARATIVE-EXAMPLE" class="id_link">#</a></h4></div></div></div><p>
Suppose we are constructing a database for a large ice cream company.
The company measures peak temperatures every day as well as ice cream
sales in each region. Conceptually, we want a table like:
</p><pre class="programlisting">
CREATE TABLE measurement (
city_id int not null,
logdate date not null,
peaktemp int,
unitsales int
);
</pre><p>
We know that most queries will access just the last week's, month's or
quarter's data, since the main use of this table will be to prepare
online reports for management. To reduce the amount of old data that
needs to be stored, we decide to keep only the most recent 3 years
worth of data. At the beginning of each month we will remove the oldest
month's data. In this situation we can use partitioning to help us meet
all of our different requirements for the measurements table.
</p><p>
To use declarative partitioning in this case, use the following steps:
</p><div class="orderedlist"><ol class="orderedlist compact" type="1"><li class="listitem"><p>
Create the <code class="structname">measurement</code> table as a partitioned
table by specifying the <code class="literal">PARTITION BY</code> clause, which
includes the partitioning method (<code class="literal">RANGE</code> in this
case) and the list of column(s) to use as the partition key.
</p><pre class="programlisting">
CREATE TABLE measurement (
city_id int not null,
logdate date not null,
peaktemp int,
unitsales int
) PARTITION BY RANGE (logdate);
</pre><p>
</p></li><li class="listitem"><p>
Create partitions. Each partition's definition must specify bounds
that correspond to the partitioning method and partition key of the
parent. Note that specifying bounds such that the new partition's
values would overlap with those in one or more existing partitions will
cause an error.
</p><p>
Partitions thus created are in every way normal
<span class="productname">PostgreSQL</span>
tables (or, possibly, foreign tables). It is possible to specify a
tablespace and storage parameters for each partition separately.
</p><p>
For our example, each partition should hold one month's worth of
data, to match the requirement of deleting one month's data at a
time. So the commands might look like:
</p><pre class="programlisting">
CREATE TABLE measurement_y2006m02 PARTITION OF measurement
FOR VALUES FROM ('2006-02-01') TO ('2006-03-01');
CREATE TABLE measurement_y2006m03 PARTITION OF measurement
FOR VALUES FROM ('2006-03-01') TO ('2006-04-01');
...
CREATE TABLE measurement_y2007m11 PARTITION OF measurement
FOR VALUES FROM ('2007-11-01') TO ('2007-12-01');
CREATE TABLE measurement_y2007m12 PARTITION OF measurement
FOR VALUES FROM ('2007-12-01') TO ('2008-01-01')
TABLESPACE fasttablespace;
CREATE TABLE measurement_y2008m01 PARTITION OF measurement
FOR VALUES FROM ('2008-01-01') TO ('2008-02-01')
WITH (parallel_workers = 4)
TABLESPACE fasttablespace;
</pre><p>
(Recall that adjacent partitions can share a bound value, since
range upper bounds are treated as exclusive bounds.)
</p><p>
If you wish to implement sub-partitioning, again specify the
<code class="literal">PARTITION BY</code> clause in the commands used to create
individual partitions, for example:
</p><pre class="programlisting">
CREATE TABLE measurement_y2006m02 PARTITION OF measurement
FOR VALUES FROM ('2006-02-01') TO ('2006-03-01')
PARTITION BY RANGE (peaktemp);
</pre><p>
After creating partitions of <code class="structname">measurement_y2006m02</code>,
any data inserted into <code class="structname">measurement</code> that is mapped to
<code class="structname">measurement_y2006m02</code> (or data that is
directly inserted into <code class="structname">measurement_y2006m02</code>,
which is allowed provided its partition constraint is satisfied)
will be further redirected to one of its
partitions based on the <code class="structfield">peaktemp</code> column. The partition
key specified may overlap with the parent's partition key, although
care should be taken when specifying the bounds of a sub-partition
such that the set of data it accepts constitutes a subset of what
the partition's own bounds allow; the system does not try to check
whether that's really the case.
</p><p>
Inserting data into the parent table that does not map
to one of the existing partitions will cause an error; an appropriate
partition must be added manually.
</p><p>
It is not necessary to manually create table constraints describing
the partition boundary conditions for partitions. Such constraints
will be created automatically.
</p></li><li class="listitem"><p>
Create an index on the key column(s), as well as any other indexes you
might want, on the partitioned table. (The key index is not strictly
necessary, but in most scenarios it is helpful.)
This automatically creates a matching index on each partition, and
any partitions you create or attach later will also have such an
index.
An index or unique constraint declared on a partitioned table
is <span class="quote">“<span class="quote">virtual</span>”</span> in the same way that the partitioned table
is: the actual data is in child indexes on the individual partition
tables.
</p><pre class="programlisting">
CREATE INDEX ON measurement (logdate);
</pre><p>
</p></li><li class="listitem"><p>
Ensure that the <a class="xref" href="runtime-config-query.html#GUC-ENABLE-PARTITION-PRUNING">enable_partition_pruning</a>
configuration parameter is not disabled in <code class="filename">postgresql.conf</code>.
If it is, queries will not be optimized as desired.
</p></li></ol></div><p>
</p><p>
In the above example we would be creating a new partition each month, so
it might be wise to write a script that generates the required DDL
automatically.
</p></div><div class="sect3" id="DDL-PARTITIONING-DECLARATIVE-MAINTENANCE"><div class="titlepage"><div><div><h4 class="title">5.11.2.2. Partition Maintenance <a href="#DDL-PARTITIONING-DECLARATIVE-MAINTENANCE" class="id_link">#</a></h4></div></div></div><p>
Normally the set of partitions established when initially defining the
table is not intended to remain static. It is common to want to
remove partitions holding old data and periodically add new partitions for
new data. One of the most important advantages of partitioning is
precisely that it allows this otherwise painful task to be executed
nearly instantaneously by manipulating the partition structure, rather
than physically moving large amounts of data around.
</p><p>
The simplest option for removing old data is to drop the partition that
is no longer necessary:
</p><pre class="programlisting">
DROP TABLE measurement_y2006m02;
</pre><p>
This can very quickly delete millions of records because it doesn't have
to individually delete every record. Note however that the above command
requires taking an <code class="literal">ACCESS EXCLUSIVE</code> lock on the parent
table.
</p><p>
Another option that is often preferable is to remove the partition from
the partitioned table but retain access to it as a table in its own
right. This has two forms:
</p><pre class="programlisting">
ALTER TABLE measurement DETACH PARTITION measurement_y2006m02;
ALTER TABLE measurement DETACH PARTITION measurement_y2006m02 CONCURRENTLY;
</pre><p>
These allow further operations to be performed on the data before
it is dropped. For example, this is often a useful time to back up
the data using <code class="command">COPY</code>, <span class="application">pg_dump</span>, or
similar tools. It might also be a useful time to aggregate data
into smaller formats, perform other data manipulations, or run
reports. The first form of the command requires an
<code class="literal">ACCESS EXCLUSIVE</code> lock on the parent table.
Adding the <code class="literal">CONCURRENTLY</code> qualifier as in the second
form allows the detach operation to require only
<code class="literal">SHARE UPDATE EXCLUSIVE</code> lock on the parent table, but see
<a class="link" href="sql-altertable.html#SQL-ALTERTABLE-DETACH-PARTITION"><code class="literal">ALTER TABLE ... DETACH PARTITION</code></a>
for details on the restrictions.
</p><p>
Similarly we can add a new partition to handle new data. We can create an
empty partition in the partitioned table just as the original partitions
were created above:
</p><pre class="programlisting">
CREATE TABLE measurement_y2008m02 PARTITION OF measurement
FOR VALUES FROM ('2008-02-01') TO ('2008-03-01')
TABLESPACE fasttablespace;
</pre><p>
As an alternative, it is sometimes more convenient to create the
new table outside the partition structure, and attach it as a
partition later. This allows new data to be loaded, checked, and
transformed prior to it appearing in the partitioned table.
Moreover, the <code class="literal">ATTACH PARTITION</code> operation requires
only <code class="literal">SHARE UPDATE EXCLUSIVE</code> lock on the
partitioned table, as opposed to the <code class="literal">ACCESS
EXCLUSIVE</code> lock that is required by <code class="command">CREATE TABLE
... PARTITION OF</code>, so it is more friendly to concurrent
operations on the partitioned table.
The <code class="literal">CREATE TABLE ... LIKE</code> option is helpful
to avoid tediously repeating the parent table's definition:
</p><pre class="programlisting">
CREATE TABLE measurement_y2008m02
(LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS)
TABLESPACE fasttablespace;
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement ATTACH PARTITION measurement_y2008m02
FOR VALUES FROM ('2008-02-01') TO ('2008-03-01' );
</pre><p>
</p><p>
Before running the <code class="command">ATTACH PARTITION</code> command, it is
recommended to create a <code class="literal">CHECK</code> constraint on the table to
be attached that matches the expected partition constraint, as
illustrated above. That way, the system will be able to skip the scan
which is otherwise needed to validate the implicit
partition constraint. Without the <code class="literal">CHECK</code> constraint,
the table will be scanned to validate the partition constraint while
holding an <code class="literal">ACCESS EXCLUSIVE</code> lock on that partition.
It is recommended to drop the now-redundant <code class="literal">CHECK</code>
constraint after the <code class="command">ATTACH PARTITION</code> is complete. If
the table being attached is itself a partitioned table, then each of its
sub-partitions will be recursively locked and scanned until either a
suitable <code class="literal">CHECK</code> constraint is encountered or the leaf
partitions are reached.
</p><p>
Similarly, if the partitioned table has a <code class="literal">DEFAULT</code>
partition, it is recommended to create a <code class="literal">CHECK</code>
constraint which excludes the to-be-attached partition's constraint. If
this is not done then the <code class="literal">DEFAULT</code> partition will be
scanned to verify that it contains no records which should be located in
the partition being attached. This operation will be performed whilst
holding an <code class="literal">ACCESS EXCLUSIVE</code> lock on the <code class="literal">
DEFAULT</code> partition. If the <code class="literal">DEFAULT</code> partition
is itself a partitioned table, then each of its partitions will be
recursively checked in the same way as the table being attached, as
mentioned above.
</p><p>
As explained above, it is possible to create indexes on partitioned tables
so that they are applied automatically to the entire hierarchy.
This is very
convenient, as not only will the existing partitions become indexed, but
also any partitions that are created in the future will. One limitation is
that it's not possible to use the <code class="literal">CONCURRENTLY</code>
qualifier when creating such a partitioned index. To avoid long lock
times, it is possible to use <code class="command">CREATE INDEX ON ONLY</code>
the partitioned table; such an index is marked invalid, and the partitions
do not get the index applied automatically. The indexes on partitions can
be created individually using <code class="literal">CONCURRENTLY</code>, and then
<em class="firstterm">attached</em> to the index on the parent using
<code class="command">ALTER INDEX .. ATTACH PARTITION</code>. Once indexes for all
partitions are attached to the parent index, the parent index is marked
valid automatically. Example:
</p><pre class="programlisting">
CREATE INDEX measurement_usls_idx ON ONLY measurement (unitsales);
CREATE INDEX CONCURRENTLY measurement_usls_200602_idx
ON measurement_y2006m02 (unitsales);
ALTER INDEX measurement_usls_idx
ATTACH PARTITION measurement_usls_200602_idx;
...
</pre><p>
This technique can be used with <code class="literal">UNIQUE</code> and
<code class="literal">PRIMARY KEY</code> constraints too; the indexes are created
implicitly when the constraint is created. Example:
</p><pre class="programlisting">
ALTER TABLE ONLY measurement ADD UNIQUE (city_id, logdate);
ALTER TABLE measurement_y2006m02 ADD UNIQUE (city_id, logdate);
ALTER INDEX measurement_city_id_logdate_key
ATTACH PARTITION measurement_y2006m02_city_id_logdate_key;
...
</pre><p>
</p></div><div class="sect3" id="DDL-PARTITIONING-DECLARATIVE-LIMITATIONS"><div class="titlepage"><div><div><h4 class="title">5.11.2.3. Limitations <a href="#DDL-PARTITIONING-DECLARATIVE-LIMITATIONS" class="id_link">#</a></h4></div></div></div><p>
The following limitations apply to partitioned tables:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
To create a unique or primary key constraint on a partitioned table,
the partition keys must not include any expressions or function calls
and the constraint's columns must include all of the partition key
columns. This limitation exists because the individual indexes making
up the constraint can only directly enforce uniqueness within their own
partitions; therefore, the partition structure itself must guarantee
that there are not duplicates in different partitions.
</p></li><li class="listitem"><p>
There is no way to create an exclusion constraint spanning the
whole partitioned table. It is only possible to put such a
constraint on each leaf partition individually. Again, this
limitation stems from not being able to enforce cross-partition
restrictions.
</p></li><li class="listitem"><p>
<code class="literal">BEFORE ROW</code> triggers on <code class="literal">INSERT</code>
cannot change which partition is the final destination for a new row.
</p></li><li class="listitem"><p>
Mixing temporary and permanent relations in the same partition tree is
not allowed. Hence, if the partitioned table is permanent, so must be
its partitions and likewise if the partitioned table is temporary. When
using temporary relations, all members of the partition tree have to be
from the same session.
</p></li></ul></div><p>
</p><p>
Individual partitions are linked to their partitioned table using
inheritance behind-the-scenes. However, it is not possible to use
all of the generic features of inheritance with declaratively
partitioned tables or their partitions, as discussed below. Notably,
a partition cannot have any parents other than the partitioned table
it is a partition of, nor can a table inherit from both a partitioned
table and a regular table. That means partitioned tables and their
partitions never share an inheritance hierarchy with regular tables.
</p><p>
Since a partition hierarchy consisting of the partitioned table and its
partitions is still an inheritance hierarchy,
<code class="structfield">tableoid</code> and all the normal rules of
inheritance apply as described in <a class="xref" href="ddl-inherit.html" title="5.10. Inheritance">Section 5.10</a>, with
a few exceptions:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
Partitions cannot have columns that are not present in the parent. It
is not possible to specify columns when creating partitions with
<code class="command">CREATE TABLE</code>, nor is it possible to add columns to
partitions after-the-fact using <code class="command">ALTER TABLE</code>.
Tables may be added as a partition with <code class="command">ALTER TABLE
... ATTACH PARTITION</code> only if their columns exactly match
the parent.
</p></li><li class="listitem"><p>
Both <code class="literal">CHECK</code> and <code class="literal">NOT NULL</code>
constraints of a partitioned table are always inherited by all its
partitions. <code class="literal">CHECK</code> constraints that are marked
<code class="literal">NO INHERIT</code> are not allowed to be created on
partitioned tables.
You cannot drop a <code class="literal">NOT NULL</code> constraint on a
partition's column if the same constraint is present in the parent
table.
</p></li><li class="listitem"><p>
Using <code class="literal">ONLY</code> to add or drop a constraint on only
the partitioned table is supported as long as there are no
partitions. Once partitions exist, using <code class="literal">ONLY</code>
will result in an error for any constraints other than
<code class="literal">UNIQUE</code> and <code class="literal">PRIMARY KEY</code>.
Instead, constraints on the partitions
themselves can be added and (if they are not present in the parent
table) dropped.
</p></li><li class="listitem"><p>
As a partitioned table does not have any data itself, attempts to use
<code class="command">TRUNCATE</code> <code class="literal">ONLY</code> on a partitioned
table will always return an error.
</p></li></ul></div><p>
</p></div></div><div class="sect2" id="DDL-PARTITIONING-USING-INHERITANCE"><div class="titlepage"><div><div><h3 class="title">5.11.3. Partitioning Using Inheritance <a href="#DDL-PARTITIONING-USING-INHERITANCE" class="id_link">#</a></h3></div></div></div><p>
While the built-in declarative partitioning is suitable for most
common use cases, there are some circumstances where a more flexible
approach may be useful. Partitioning can be implemented using table
inheritance, which allows for several features not supported
by declarative partitioning, such as:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
For declarative partitioning, partitions must have exactly the same set
of columns as the partitioned table, whereas with table inheritance,
child tables may have extra columns not present in the parent.
</p></li><li class="listitem"><p>
Table inheritance allows for multiple inheritance.
</p></li><li class="listitem"><p>
Declarative partitioning only supports range, list and hash
partitioning, whereas table inheritance allows data to be divided in a
manner of the user's choosing. (Note, however, that if constraint
exclusion is unable to prune child tables effectively, query performance
might be poor.)
</p></li></ul></div><p>
</p><div class="sect3" id="DDL-PARTITIONING-INHERITANCE-EXAMPLE"><div class="titlepage"><div><div><h4 class="title">5.11.3.1. Example <a href="#DDL-PARTITIONING-INHERITANCE-EXAMPLE" class="id_link">#</a></h4></div></div></div><p>
This example builds a partitioning structure equivalent to the
declarative partitioning example above. Use
the following steps:
</p><div class="orderedlist"><ol class="orderedlist compact" type="1"><li class="listitem"><p>
Create the <span class="quote">“<span class="quote">root</span>”</span> table, from which all of the
<span class="quote">“<span class="quote">child</span>”</span> tables will inherit. This table will contain no data. Do not
define any check constraints on this table, unless you intend them
to be applied equally to all child tables. There is no point in
defining any indexes or unique constraints on it, either. For our
example, the root table is the <code class="structname">measurement</code>
table as originally defined:
</p><pre class="programlisting">
CREATE TABLE measurement (
city_id int not null,
logdate date not null,
peaktemp int,
unitsales int
);
</pre><p>
</p></li><li class="listitem"><p>
Create several <span class="quote">“<span class="quote">child</span>”</span> tables that each inherit from
the root table. Normally, these tables will not add any columns
to the set inherited from the root. Just as with declarative
partitioning, these tables are in every way normal
<span class="productname">PostgreSQL</span> tables (or foreign tables).
</p><p>
</p><pre class="programlisting">
CREATE TABLE measurement_y2006m02 () INHERITS (measurement);
CREATE TABLE measurement_y2006m03 () INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 () INHERITS (measurement);
CREATE TABLE measurement_y2007m12 () INHERITS (measurement);
CREATE TABLE measurement_y2008m01 () INHERITS (measurement);
</pre><p>
</p></li><li class="listitem"><p>
Add non-overlapping table constraints to the child tables to
define the allowed key values in each.
</p><p>
Typical examples would be:
</p><pre class="programlisting">
CHECK ( x = 1 )
CHECK ( county IN ( 'Oxfordshire', 'Buckinghamshire', 'Warwickshire' ))
CHECK ( outletID >= 100 AND outletID < 200 )
</pre><p>
Ensure that the constraints guarantee that there is no overlap
between the key values permitted in different child tables. A common
mistake is to set up range constraints like:
</p><pre class="programlisting">
CHECK ( outletID BETWEEN 100 AND 200 )
CHECK ( outletID BETWEEN 200 AND 300 )
</pre><p>
This is wrong since it is not clear which child table the key
value 200 belongs in.
Instead, ranges should be defined in this style:
</p><pre class="programlisting">
CREATE TABLE measurement_y2006m02 (
CHECK ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2006m03 (
CHECK ( logdate >= DATE '2006-03-01' AND logdate < DATE '2006-04-01' )
) INHERITS (measurement);
...
CREATE TABLE measurement_y2007m11 (
CHECK ( logdate >= DATE '2007-11-01' AND logdate < DATE '2007-12-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2007m12 (
CHECK ( logdate >= DATE '2007-12-01' AND logdate < DATE '2008-01-01' )
) INHERITS (measurement);
CREATE TABLE measurement_y2008m01 (
CHECK ( logdate >= DATE '2008-01-01' AND logdate < DATE '2008-02-01' )
) INHERITS (measurement);
</pre><p>
</p></li><li class="listitem"><p>
For each child table, create an index on the key column(s),
as well as any other indexes you might want.
</p><pre class="programlisting">
CREATE INDEX measurement_y2006m02_logdate ON measurement_y2006m02 (logdate);
CREATE INDEX measurement_y2006m03_logdate ON measurement_y2006m03 (logdate);
CREATE INDEX measurement_y2007m11_logdate ON measurement_y2007m11 (logdate);
CREATE INDEX measurement_y2007m12_logdate ON measurement_y2007m12 (logdate);
CREATE INDEX measurement_y2008m01_logdate ON measurement_y2008m01 (logdate);
</pre><p>
</p></li><li class="listitem"><p>
We want our application to be able to say <code class="literal">INSERT INTO
measurement ...</code> and have the data be redirected into the
appropriate child table. We can arrange that by attaching
a suitable trigger function to the root table.
If data will be added only to the latest child, we can
use a very simple trigger function:
</p><pre class="programlisting">
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
</pre><p>
</p><p>
After creating the function, we create a trigger which
calls the trigger function:
</p><pre class="programlisting">
CREATE TRIGGER insert_measurement_trigger
BEFORE INSERT ON measurement
FOR EACH ROW EXECUTE FUNCTION measurement_insert_trigger();
</pre><p>
We must redefine the trigger function each month so that it always
inserts into the current child table. The trigger definition does
not need to be updated, however.
</p><p>
We might want to insert data and have the server automatically
locate the child table into which the row should be added. We
could do this with a more complex trigger function, for example:
</p><pre class="programlisting">
CREATE OR REPLACE FUNCTION measurement_insert_trigger()
RETURNS TRIGGER AS $$
BEGIN
IF ( NEW.logdate >= DATE '2006-02-01' AND
NEW.logdate < DATE '2006-03-01' ) THEN
INSERT INTO measurement_y2006m02 VALUES (NEW.*);
ELSIF ( NEW.logdate >= DATE '2006-03-01' AND
NEW.logdate < DATE '2006-04-01' ) THEN
INSERT INTO measurement_y2006m03 VALUES (NEW.*);
...
ELSIF ( NEW.logdate >= DATE '2008-01-01' AND
NEW.logdate < DATE '2008-02-01' ) THEN
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
ELSE
RAISE EXCEPTION 'Date out of range. Fix the measurement_insert_trigger() function!';
END IF;
RETURN NULL;
END;
$$
LANGUAGE plpgsql;
</pre><p>
The trigger definition is the same as before.
Note that each <code class="literal">IF</code> test must exactly match the
<code class="literal">CHECK</code> constraint for its child table.
</p><p>
While this function is more complex than the single-month case,
it doesn't need to be updated as often, since branches can be
added in advance of being needed.
</p><div class="note"><h3 class="title">Note</h3><p>
In practice, it might be best to check the newest child first,
if most inserts go into that child. For simplicity, we have
shown the trigger's tests in the same order as in other parts
of this example.
</p></div><p>
A different approach to redirecting inserts into the appropriate
child table is to set up rules, instead of a trigger, on the
root table. For example:
</p><pre class="programlisting">
CREATE RULE measurement_insert_y2006m02 AS
ON INSERT TO measurement WHERE
( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' )
DO INSTEAD
INSERT INTO measurement_y2006m02 VALUES (NEW.*);
...
CREATE RULE measurement_insert_y2008m01 AS
ON INSERT TO measurement WHERE
( logdate >= DATE '2008-01-01' AND logdate < DATE '2008-02-01' )
DO INSTEAD
INSERT INTO measurement_y2008m01 VALUES (NEW.*);
</pre><p>
A rule has significantly more overhead than a trigger, but the
overhead is paid once per query rather than once per row, so this
method might be advantageous for bulk-insert situations. In most
cases, however, the trigger method will offer better performance.
</p><p>
Be aware that <code class="command">COPY</code> ignores rules. If you want to
use <code class="command">COPY</code> to insert data, you'll need to copy into the
correct child table rather than directly into the root. <code class="command">COPY</code>
does fire triggers, so you can use it normally if you use the trigger
approach.
</p><p>
Another disadvantage of the rule approach is that there is no simple
way to force an error if the set of rules doesn't cover the insertion
date; the data will silently go into the root table instead.
</p></li><li class="listitem"><p>
Ensure that the <a class="xref" href="runtime-config-query.html#GUC-CONSTRAINT-EXCLUSION">constraint_exclusion</a>
configuration parameter is not disabled in
<code class="filename">postgresql.conf</code>; otherwise
child tables may be accessed unnecessarily.
</p></li></ol></div><p>
</p><p>
As we can see, a complex table hierarchy could require a
substantial amount of DDL. In the above example we would be creating
a new child table each month, so it might be wise to write a script that
generates the required DDL automatically.
</p></div><div class="sect3" id="DDL-PARTITIONING-INHERITANCE-MAINTENANCE"><div class="titlepage"><div><div><h4 class="title">5.11.3.2. Maintenance for Inheritance Partitioning <a href="#DDL-PARTITIONING-INHERITANCE-MAINTENANCE" class="id_link">#</a></h4></div></div></div><p>
To remove old data quickly, simply drop the child table that is no longer
necessary:
</p><pre class="programlisting">
DROP TABLE measurement_y2006m02;
</pre><p>
</p><p>
To remove the child table from the inheritance hierarchy table but retain access to
it as a table in its own right:
</p><pre class="programlisting">
ALTER TABLE measurement_y2006m02 NO INHERIT measurement;
</pre><p>
</p><p>
To add a new child table to handle new data, create an empty child table
just as the original children were created above:
</p><pre class="programlisting">
CREATE TABLE measurement_y2008m02 (
CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' )
) INHERITS (measurement);
</pre><p>
Alternatively, one may want to create and populate the new child table
before adding it to the table hierarchy. This could allow data to be
loaded, checked, and transformed before being made visible to queries on
the parent table.
</p><pre class="programlisting">
CREATE TABLE measurement_y2008m02
(LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS);
ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' );
\copy measurement_y2008m02 from 'measurement_y2008m02'
-- possibly some other data preparation work
ALTER TABLE measurement_y2008m02 INHERIT measurement;
</pre><p>
</p></div><div class="sect3" id="DDL-PARTITIONING-INHERITANCE-CAVEATS"><div class="titlepage"><div><div><h4 class="title">5.11.3.3. Caveats <a href="#DDL-PARTITIONING-INHERITANCE-CAVEATS" class="id_link">#</a></h4></div></div></div><p>
The following caveats apply to partitioning implemented using
inheritance:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
There is no automatic way to verify that all of the
<code class="literal">CHECK</code> constraints are mutually
exclusive. It is safer to create code that generates
child tables and creates and/or modifies associated objects than
to write each by hand.
</p></li><li class="listitem"><p>
Indexes and foreign key constraints apply to single tables and not
to their inheritance children, hence they have some
<a class="link" href="ddl-inherit.html#DDL-INHERIT-CAVEATS" title="5.10.1. Caveats">caveats</a> to be aware of.
</p></li><li class="listitem"><p>
The schemes shown here assume that the values of a row's key column(s)
never change, or at least do not change enough to require it to move to another partition.
An <code class="command">UPDATE</code> that attempts
to do that will fail because of the <code class="literal">CHECK</code> constraints.
If you need to handle such cases, you can put suitable update triggers
on the child tables, but it makes management of the structure
much more complicated.
</p></li><li class="listitem"><p>
If you are using manual <code class="command">VACUUM</code> or
<code class="command">ANALYZE</code> commands, don't forget that
you need to run them on each child table individually. A command like:
</p><pre class="programlisting">
ANALYZE measurement;
</pre><p>
will only process the root table.
</p></li><li class="listitem"><p>
<code class="command">INSERT</code> statements with <code class="literal">ON CONFLICT</code>
clauses are unlikely to work as expected, as the <code class="literal">ON CONFLICT</code>
action is only taken in case of unique violations on the specified
target relation, not its child relations.
</p></li><li class="listitem"><p>
Triggers or rules will be needed to route rows to the desired
child table, unless the application is explicitly aware of the
partitioning scheme. Triggers may be complicated to write, and will
be much slower than the tuple routing performed internally by
declarative partitioning.
</p></li></ul></div><p>
</p></div></div><div class="sect2" id="DDL-PARTITION-PRUNING"><div class="titlepage"><div><div><h3 class="title">5.11.4. Partition Pruning <a href="#DDL-PARTITION-PRUNING" class="id_link">#</a></h3></div></div></div><a id="id-1.5.4.13.9.2" class="indexterm"></a><p>
<em class="firstterm">Partition pruning</em> is a query optimization technique
that improves performance for declaratively partitioned tables.
As an example:
</p><pre class="programlisting">
SET enable_partition_pruning = on; -- the default
SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01';
</pre><p>
Without partition pruning, the above query would scan each of the
partitions of the <code class="structname">measurement</code> table. With
partition pruning enabled, the planner will examine the definition
of each partition and prove that the partition need not
be scanned because it could not contain any rows meeting the query's
<code class="literal">WHERE</code> clause. When the planner can prove this, it
excludes (<em class="firstterm">prunes</em>) the partition from the query
plan.
</p><p>
By using the EXPLAIN command and the <a class="xref" href="runtime-config-query.html#GUC-ENABLE-PARTITION-PRUNING">enable_partition_pruning</a> configuration parameter, it's
possible to show the difference between a plan for which partitions have
been pruned and one for which they have not. A typical unoptimized
plan for this type of table setup is:
</p><pre class="programlisting">
SET enable_partition_pruning = off;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01';
QUERY PLAN
-----------------------------------------------------------------------------------
Aggregate (cost=188.76..188.77 rows=1 width=8)
-> Append (cost=0.00..181.05 rows=3085 width=0)
-> Seq Scan on measurement_y2006m02 (cost=0.00..33.12 rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
-> Seq Scan on measurement_y2006m03 (cost=0.00..33.12 rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
...
-> Seq Scan on measurement_y2007m11 (cost=0.00..33.12 rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
-> Seq Scan on measurement_y2007m12 (cost=0.00..33.12 rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
-> Seq Scan on measurement_y2008m01 (cost=0.00..33.12 rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
</pre><p>
Some or all of the partitions might use index scans instead of
full-table sequential scans, but the point here is that there
is no need to scan the older partitions at all to answer this query.
When we enable partition pruning, we get a significantly
cheaper plan that will deliver the same answer:
</p><pre class="programlisting">
SET enable_partition_pruning = on;
EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01';
QUERY PLAN
-----------------------------------------------------------------------------------
Aggregate (cost=37.75..37.76 rows=1 width=8)
-> Seq Scan on measurement_y2008m01 (cost=0.00..33.12 rows=617 width=0)
Filter: (logdate >= '2008-01-01'::date)
</pre><p>
</p><p>
Note that partition pruning is driven only by the constraints defined
implicitly by the partition keys, not by the presence of indexes.
Therefore it isn't necessary to define indexes on the key columns.
Whether an index needs to be created for a given partition depends on
whether you expect that queries that scan the partition will
generally scan a large part of the partition or just a small part.
An index will be helpful in the latter case but not the former.
</p><p>
Partition pruning can be performed not only during the planning of a
given query, but also during its execution. This is useful as it can
allow more partitions to be pruned when clauses contain expressions
whose values are not known at query planning time, for example,
parameters defined in a <code class="command">PREPARE</code> statement, using a
value obtained from a subquery, or using a parameterized value on the
inner side of a nested loop join. Partition pruning during execution
can be performed at any of the following times:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
During initialization of the query plan. Partition pruning can be
performed here for parameter values which are known during the
initialization phase of execution. Partitions which are pruned
during this stage will not show up in the query's
<code class="command">EXPLAIN</code> or <code class="command">EXPLAIN ANALYZE</code>.
It is possible to determine the number of partitions which were
removed during this phase by observing the
<span class="quote">“<span class="quote">Subplans Removed</span>”</span> property in the
<code class="command">EXPLAIN</code> output.
</p></li><li class="listitem"><p>
During actual execution of the query plan. Partition pruning may
also be performed here to remove partitions using values which are
only known during actual query execution. This includes values
from subqueries and values from execution-time parameters such as
those from parameterized nested loop joins. Since the value of
these parameters may change many times during the execution of the
query, partition pruning is performed whenever one of the
execution parameters being used by partition pruning changes.
Determining if partitions were pruned during this phase requires
careful inspection of the <code class="literal">loops</code> property in
the <code class="command">EXPLAIN ANALYZE</code> output. Subplans
corresponding to different partitions may have different values
for it depending on how many times each of them was pruned during
execution. Some may be shown as <code class="literal">(never executed)</code>
if they were pruned every time.
</p></li></ul></div><p>
</p><p>
Partition pruning can be disabled using the
<a class="xref" href="runtime-config-query.html#GUC-ENABLE-PARTITION-PRUNING">enable_partition_pruning</a> setting.
</p></div><div class="sect2" id="DDL-PARTITIONING-CONSTRAINT-EXCLUSION"><div class="titlepage"><div><div><h3 class="title">5.11.5. Partitioning and Constraint Exclusion <a href="#DDL-PARTITIONING-CONSTRAINT-EXCLUSION" class="id_link">#</a></h3></div></div></div><a id="id-1.5.4.13.10.2" class="indexterm"></a><p>
<em class="firstterm">Constraint exclusion</em> is a query optimization
technique similar to partition pruning. While it is primarily used
for partitioning implemented using the legacy inheritance method, it can be
used for other purposes, including with declarative partitioning.
</p><p>
Constraint exclusion works in a very similar way to partition
pruning, except that it uses each table's <code class="literal">CHECK</code>
constraints — which gives it its name — whereas partition
pruning uses the table's partition bounds, which exist only in the
case of declarative partitioning. Another difference is that
constraint exclusion is only applied at plan time; there is no attempt
to remove partitions at execution time.
</p><p>
The fact that constraint exclusion uses <code class="literal">CHECK</code>
constraints, which makes it slow compared to partition pruning, can
sometimes be used as an advantage: because constraints can be defined
even on declaratively-partitioned tables, in addition to their internal
partition bounds, constraint exclusion may be able
to elide additional partitions from the query plan.
</p><p>
The default (and recommended) setting of
<a class="xref" href="runtime-config-query.html#GUC-CONSTRAINT-EXCLUSION">constraint_exclusion</a> is neither
<code class="literal">on</code> nor <code class="literal">off</code>, but an intermediate setting
called <code class="literal">partition</code>, which causes the technique to be
applied only to queries that are likely to be working on inheritance partitioned
tables. The <code class="literal">on</code> setting causes the planner to examine
<code class="literal">CHECK</code> constraints in all queries, even simple ones that
are unlikely to benefit.
</p><p>
The following caveats apply to constraint exclusion:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
Constraint exclusion is only applied during query planning, unlike
partition pruning, which can also be applied during query execution.
</p></li><li class="listitem"><p>
Constraint exclusion only works when the query's <code class="literal">WHERE</code>
clause contains constants (or externally supplied parameters).
For example, a comparison against a non-immutable function such as
<code class="function">CURRENT_TIMESTAMP</code> cannot be optimized, since the
planner cannot know which child table the function's value might fall
into at run time.
</p></li><li class="listitem"><p>
Keep the partitioning constraints simple, else the planner may not be
able to prove that child tables might not need to be visited. Use simple
equality conditions for list partitioning, or simple
range tests for range partitioning, as illustrated in the preceding
examples. A good rule of thumb is that partitioning constraints should
contain only comparisons of the partitioning column(s) to constants
using B-tree-indexable operators, because only B-tree-indexable
column(s) are allowed in the partition key.
</p></li><li class="listitem"><p>
All constraints on all children of the parent table are examined
during constraint exclusion, so large numbers of children are likely
to increase query planning time considerably. So the legacy
inheritance based partitioning will work well with up to perhaps a
hundred child tables; don't try to use many thousands of children.
</p></li></ul></div><p>
</p></div><div class="sect2" id="DDL-PARTITIONING-DECLARATIVE-BEST-PRACTICES"><div class="titlepage"><div><div><h3 class="title">5.11.6. Best Practices for Declarative Partitioning <a href="#DDL-PARTITIONING-DECLARATIVE-BEST-PRACTICES" class="id_link">#</a></h3></div></div></div><p>
The choice of how to partition a table should be made carefully, as the
performance of query planning and execution can be negatively affected by
poor design.
</p><p>
One of the most critical design decisions will be the column or columns
by which you partition your data. Often the best choice will be to
partition by the column or set of columns which most commonly appear in
<code class="literal">WHERE</code> clauses of queries being executed on the
partitioned table. <code class="literal">WHERE</code> clauses that are compatible
with the partition bound constraints can be used to prune unneeded
partitions. However, you may be forced into making other decisions by
requirements for the <code class="literal">PRIMARY KEY</code> or a
<code class="literal">UNIQUE</code> constraint. Removal of unwanted data is also a
factor to consider when planning your partitioning strategy. An entire
partition can be detached fairly quickly, so it may be beneficial to
design the partition strategy in such a way that all data to be removed
at once is located in a single partition.
</p><p>
Choosing the target number of partitions that the table should be divided
into is also a critical decision to make. Not having enough partitions
may mean that indexes remain too large and that data locality remains poor
which could result in low cache hit ratios. However, dividing the table
into too many partitions can also cause issues. Too many partitions can
mean longer query planning times and higher memory consumption during both
query planning and execution, as further described below.
When choosing how to partition your table,
it's also important to consider what changes may occur in the future. For
example, if you choose to have one partition per customer and you
currently have a small number of large customers, consider the
implications if in several years you instead find yourself with a large
number of small customers. In this case, it may be better to choose to
partition by <code class="literal">HASH</code> and choose a reasonable number of
partitions rather than trying to partition by <code class="literal">LIST</code> and
hoping that the number of customers does not increase beyond what it is
practical to partition the data by.
</p><p>
Sub-partitioning can be useful to further divide partitions that are
expected to become larger than other partitions.
Another option is to use range partitioning with multiple columns in
the partition key.
Either of these can easily lead to excessive numbers of partitions,
so restraint is advisable.
</p><p>
It is important to consider the overhead of partitioning during
query planning and execution. The query planner is generally able to
handle partition hierarchies with up to a few thousand partitions fairly
well, provided that typical queries allow the query planner to prune all
but a small number of partitions. Planning times become longer and memory
consumption becomes higher when more partitions remain after the planner
performs partition pruning. Another
reason to be concerned about having a large number of partitions is that
the server's memory consumption may grow significantly over
time, especially if many sessions touch large numbers of partitions.
That's because each partition requires its metadata to be loaded into the
local memory of each session that touches it.
</p><p>
With data warehouse type workloads, it can make sense to use a larger
number of partitions than with an <acronym class="acronym">OLTP</acronym> type workload.
Generally, in data warehouses, query planning time is less of a concern as
the majority of processing time is spent during query execution. With
either of these two types of workload, it is important to make the right
decisions early, as re-partitioning large quantities of data can be
painfully slow. Simulations of the intended workload are often beneficial
for optimizing the partitioning strategy. Never just assume that more
partitions are better than fewer partitions, nor vice-versa.
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